Machine learning techniques can be employed to enable computers to process empirical data and draw conclusions thereon. One example machine learning technique is the training of a decision tree based on example data, and applying the trained decision tree to classify unknown data into one of several classes. In many applications, more accurate results may be obtained by using as large a data set as possible for the training of the decision tree. However, one drawback of training decision trees with large data sets is that such training can overwhelm the processor or memory resources of a computing system, thereby making the training of the decision tree impractical or impossible. As a result, computer scientists and software developers are limited in the size and complexity of the data sets that they can use for training decision trees, and improvements in the classification ability of such decision trees are difficult to come by.